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Model-based hand tracking using a hierarchical Bayesian filter

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4 Author(s)
Stenger, B. ; Toshiba Corp. R&D Center, Kawasaki ; Thayananthan, A. ; Torr, P.H.S. ; Cipolla, R.

This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 9 )